import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
#clustering model library
from sklearn.cluster import KMeans
from scipy.cluster.hierarchy import linkage, dendrogram
from sklearn.cluster import AgglomerativeClusteringdata = pd.read_csv('vacation.csv')
data.drop('Unnamed: 0', axis=1, inplace=True)
data.head()
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| ID | Gender | Age | Education | Occupation | State | Relationship.Status | Obligation | Obligation2 | NEP | ... | life style of the local people | intense experience of nature | cosiness/familiar atmosphere | maintain unspoilt surroundings | everything organised | unspoilt nature/natural landscape | cultural offers | change of surroundings | Income(k$) | Expenditure | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Female | 25 | 6.0 | Clerical or service worker | VIC | single | 4.800000 | Q4 | 3.200000 | ... | no | no | no | no | no | no | no | no | 15.0 | 39.0 |
| 1 | 2 | Female | 31 | 8.0 | professional | WA | married | 3.300000 | Q1 | 3.400000 | ... | yes | no | no | no | no | no | no | yes | 15.0 | 81.0 |
| 2 | 3 | Male | 21 | 3.0 | NaN | NSW | single | 3.400000 | Q2 | 3.066667 | ... | yes | no | yes | no | no | no | yes | no | 16.0 | 6.0 |
| 3 | 4 | Female | 18 | 2.0 | unemployed | NSW | single | 2.633333 | Q1 | 3.400000 | ... | no | yes | no | no | no | no | no | yes | 16.0 | 77.0 |
| 4 | 5 | Male | 61 | 3.0 | retired | WA | married | 3.400000 | Q2 | 3.733333 | ... | yes | no | no | yes | no | no | no | no | 17.0 | 40.0 |
5 rows × 33 columns
data.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000 entries, 0 to 999
Data columns (total 33 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 ID 1000 non-null int64
1 Gender 1000 non-null object
2 Age 1000 non-null int64
3 Education 992 non-null float64
4 Occupation 941 non-null object
5 State 1000 non-null object
6 Relationship.Status 996 non-null object
7 Obligation 1000 non-null float64
8 Obligation2 1000 non-null object
9 NEP 1000 non-null float64
10 Vacation.Behaviour 975 non-null float64
11 rest and relax 1000 non-null object
12 luxury / be spoilt 1000 non-null object
13 do sports 1000 non-null object
14 excitement, a challenge 1000 non-null object
15 not exceed planned budget 1000 non-null object
16 realise creativity 1000 non-null object
17 fun and entertainment 1000 non-null object
18 good company 1000 non-null object
19 health and beauty 1000 non-null object
20 free-and-easy-going 1000 non-null object
21 entertainment facilities 1000 non-null object
22 not care about prices 1000 non-null object
23 life style of the local people 1000 non-null object
24 intense experience of nature 1000 non-null object
25 cosiness/familiar atmosphere 1000 non-null object
26 maintain unspoilt surroundings 1000 non-null object
27 everything organised 1000 non-null object
28 unspoilt nature/natural landscape 1000 non-null object
29 cultural offers 1000 non-null object
30 change of surroundings 1000 non-null object
31 Income(k$) 200 non-null float64
32 Expenditure 200 non-null float64
dtypes: float64(6), int64(2), object(25)
memory usage: 257.9+ KB
print(pd.isnull(data).sum())ID 0
Gender 0
Age 0
Education 8
Occupation 59
State 0
Relationship.Status 4
Obligation 0
Obligation2 0
NEP 0
Vacation.Behaviour 25
rest and relax 0
luxury / be spoilt 0
do sports 0
excitement, a challenge 0
not exceed planned budget 0
realise creativity 0
fun and entertainment 0
good company 0
health and beauty 0
free-and-easy-going 0
entertainment facilities 0
not care about prices 0
life style of the local people 0
intense experience of nature 0
cosiness/familiar atmosphere 0
maintain unspoilt surroundings 0
everything organised 0
unspoilt nature/natural landscape 0
cultural offers 0
change of surroundings 0
Income(k$) 800
Expenditure 800
dtype: int64
data.dropna(axis=0, how='any', thresh=None, subset=None, inplace=True)
print(pd.isnull(data).sum())ID 0
Gender 0
Age 0
Education 0
Occupation 0
State 0
Relationship.Status 0
Obligation 0
Obligation2 0
NEP 0
Vacation.Behaviour 0
rest and relax 0
luxury / be spoilt 0
do sports 0
excitement, a challenge 0
not exceed planned budget 0
realise creativity 0
fun and entertainment 0
good company 0
health and beauty 0
free-and-easy-going 0
entertainment facilities 0
not care about prices 0
life style of the local people 0
intense experience of nature 0
cosiness/familiar atmosphere 0
maintain unspoilt surroundings 0
everything organised 0
unspoilt nature/natural landscape 0
cultural offers 0
change of surroundings 0
Income(k$) 0
Expenditure 0
dtype: int64
data.describe()
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| ID | Age | Education | Obligation | NEP | Vacation.Behaviour | Income(k$) | Expenditure | |
|---|---|---|---|---|---|---|---|---|
| count | 188.000000 | 188.000000 | 188.000000 | 188.000000 | 188.000000 | 188.000000 | 188.000000 | 188.000000 |
| mean | 100.617021 | 44.776596 | 5.037234 | 3.762234 | 3.664894 | 2.936464 | 60.611702 | 51.005319 |
| std | 58.113591 | 14.118031 | 2.489270 | 0.625359 | 0.564779 | 0.661095 | 26.483842 | 25.955020 |
| min | 1.000000 | 18.000000 | 1.000000 | 1.000000 | 2.266667 | 1.551724 | 15.000000 | 1.000000 |
| 25% | 50.750000 | 35.000000 | 3.000000 | 3.433333 | 3.266667 | 2.410714 | 41.500000 | 35.000000 |
| 50% | 101.500000 | 42.000000 | 6.000000 | 3.833333 | 3.666667 | 2.929803 | 62.000000 | 50.000000 |
| 75% | 150.250000 | 58.000000 | 7.000000 | 4.141667 | 4.133333 | 3.435000 | 78.000000 | 73.000000 |
| max | 200.000000 | 84.000000 | 8.000000 | 5.000000 | 5.000000 | 4.545455 | 137.000000 | 99.000000 |
data.corr()
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| ID | Age | Education | Obligation | NEP | Vacation.Behaviour | Income(k$) | Expenditure | |
|---|---|---|---|---|---|---|---|---|
| ID | 1.000000 | 0.129412 | -0.145179 | -0.015188 | 0.017554 | 0.105743 | 0.977143 | 0.009035 |
| Age | 0.129412 | 1.000000 | -0.217661 | 0.096415 | 0.091652 | 0.088241 | 0.148038 | -0.039035 |
| Education | -0.145179 | -0.217661 | 1.000000 | -0.102149 | -0.037483 | -0.105066 | -0.161605 | 0.047340 |
| Obligation | -0.015188 | 0.096415 | -0.102149 | 1.000000 | 0.361037 | 0.481686 | -0.033803 | -0.029299 |
| NEP | 0.017554 | 0.091652 | -0.037483 | 0.361037 | 1.000000 | 0.289770 | -0.012941 | 0.008488 |
| Vacation.Behaviour | 0.105743 | 0.088241 | -0.105066 | 0.481686 | 0.289770 | 1.000000 | 0.099979 | -0.031679 |
| Income(k$) | 0.977143 | 0.148038 | -0.161605 | -0.033803 | -0.012941 | 0.099979 | 1.000000 | 0.005775 |
| Expenditure | 0.009035 | -0.039035 | 0.047340 | -0.029299 | 0.008488 | -0.031679 | 0.005775 | 1.000000 |
plt.figure(figsize=(7,7))
sns.heatmap(data.corr(), annot=True)
plt.show()
labels = ['Male','Female']
sizes = [data.query('Gender == "Male"').Gender.count(),data.query('Gender == "Female"').Gender.count()]
#colors
colors = ['#ffdaB9','#66b3ff']
#explsion
explode = (0.05,0.05)
plt.figure(figsize=(8,8))
my_circle=plt.Circle( (0,0), 0.7, color='white')
plt.pie(sizes, colors = colors, labels=labels, autopct='%1.1f%%', startangle=90, pctdistance=0.85,explode=explode)
p=plt.gcf()
plt.axis('equal')
p.gca().add_artist(my_circle)
plt.show()
plt.figure(figsize=(20,10))
sns.countplot(data.Age)
plt.xlabel("Age")
plt.ylabel("Person Count")
plt.show()/home/reddy/.local/lib/python3.10/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
warnings.warn(
plt.figure(figsize=(20,7))
gender = ['Male', 'Female']
for i in gender:
plt.scatter(x='Age',y='Income(k$)', data=data[data['Gender']==i],s = 200 , alpha = 0.5 , label = i)
plt.legend()
plt.xlabel("Age")
plt.ylabel("Annual Income")
plt.title("Annual Income according to Age")
plt.show()
plt.figure(figsize=(20,7))
gender = ['Male', 'Female']
for i in gender:
plt.scatter(x='Age',y='Expenditure', data=data[data['Gender']==i],s = 200 , alpha = 0.5 , label = i)
plt.legend()
plt.xlabel("Age")
plt.ylabel("Expenditure(k$)")
plt.title("Expenditure according to Age")
plt.show()
plt.figure(figsize=(20,7))
gender = ['Male', 'Female']
for i in gender:
plt.scatter(x='Age',y='Education', data=data[data['Gender']==i],s = 200 , alpha = 0.5 , label = i)
plt.legend()
plt.xlabel("Age")
plt.ylabel("Education")
plt.title("Education according to Age")
plt.show()
plt.figure(figsize=(20,7))
gender = ['Male', 'Female']
for i in gender:
plt.scatter(x='Income(k$)',y='Age', data=data[data['Gender']==i],s = 200 , alpha = 0.5 , label = i)
plt.legend()
plt.xlabel("Anual Income")
plt.ylabel("Age")
plt.title("Anual Income according to Age")
plt.show()
plt.figure(figsize=(20,7))
gender = ['Male', 'Female']
for i in gender:
plt.scatter(x='Expenditure',y='Income(k$)', data=data[data['Gender']==i],s = 200 , alpha = 0.5 , label = i)
plt.legend()
plt.xlabel("Expenditure")
plt.ylabel("Anual Income")
plt.title("Expenditure according to Anual Income")
plt.show()
data.replace(('yes', 'no'), (1, 0), inplace=True)
data.head()
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| ID | Gender | Age | Education | Occupation | State | Relationship.Status | Obligation | Obligation2 | NEP | ... | life style of the local people | intense experience of nature | cosiness/familiar atmosphere | maintain unspoilt surroundings | everything organised | unspoilt nature/natural landscape | cultural offers | change of surroundings | Income(k$) | Expenditure | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Female | 25 | 6.0 | Clerical or service worker | VIC | single | 4.800000 | Q4 | 3.200000 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 15.0 | 39.0 |
| 1 | 2 | Female | 31 | 8.0 | professional | WA | married | 3.300000 | Q1 | 3.400000 | ... | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 15.0 | 81.0 |
| 3 | 4 | Female | 18 | 2.0 | unemployed | NSW | single | 2.633333 | Q1 | 3.400000 | ... | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 16.0 | 77.0 |
| 4 | 5 | Male | 61 | 3.0 | retired | WA | married | 3.400000 | Q2 | 3.733333 | ... | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 17.0 | 40.0 |
| 5 | 6 | Female | 63 | 7.0 | retired | QLD | married | 4.400000 | Q4 | 4.266667 | ... | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 17.0 | 76.0 |
5 rows × 33 columns
#define k value
from sklearn import preprocessing
wcss = []
data_model = data.drop(['Gender','ID','Occupation','State', 'Relationship.Status', 'Obligation2'],axis=1)
for k in range(1,15):
kmeans = KMeans(n_clusters=k)
kmeans.fit(data_model)
wcss.append(kmeans.inertia_)
# the best value is elbow value. It's 5.
plt.figure(figsize=(15,5))
plt.plot(range(1,15),wcss)
plt.xlabel("number of k (cluster) value")
plt.ylabel("wcss")
plt.show()
#create model
kmeans = KMeans(n_clusters=5)
data_predict = kmeans.fit_predict(data_model)
plt.figure(figsize=(15,10))
plt.scatter( x = 'Income(k$)' ,y = 'Expenditure' , data = data_model , c = data_predict , s = 200 )
plt.xlabel("Annual Income (k$)")
plt.ylabel("Expenditure")
plt.show()
#create demogram and find the best clustering value
merg = linkage(data_model,method="ward")
plt.figure(figsize=(25,10))
dendrogram(merg,leaf_rotation = 90)
plt.xlabel("data points")
plt.ylabel("euclidean distance")
plt.show()
#create model
hiyerartical_cluster = AgglomerativeClustering(n_clusters = 5,affinity= "euclidean",linkage = "ward")
data_predict = hiyerartical_cluster.fit_predict(data_model)
plt.figure(figsize=(15,10))
plt.scatter( x = 'Income(k$)' ,y = 'Expenditure' , data = data_model , c = data_predict , s = 200 )
plt.show()